Research on Description and Utilization of Meta-Information in Psychological data

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The purpose of the research is to construct tools for assisting data-analysis activities in psychological study, especially usage of statistical models that represent complex relationship between variables.(a) We developed a program coded in Prolog to analyze Markov properties of directed acyclic graphs (DAG). The major research results are as follows. Two DAGs that specify the probability families of the same conditional independences are called "Markov equivalent". We succeeded to classify the statistically equivalent DAG families up to order six on small PC. Identifying causal influence of a variable to another is straight if all the related variables are observed. But in cases where some variables are unobserved, the inference is not trivial. Pearl (1995) gave probability transformation rules that deduce causal effect estimation formula. The rules contain complex inspections of Markov properties of the graph. Our software is able to deduce automatically the formula from the graph topology.(b) we surveyed the research trends in statistical metadata description. Our focus is on two XMLsed metadata formats. One is DDI (Data Documentation Initiative ) in ICPSR, which specifies an extensive format for survey codebook. Another is DandD project in Keio University by Prof. R. Shibata's research group. We used Prolog based XML parser successfully for flexible information retrieval from codebooks in XML formats.